Robust and Efficient Shape Indexing through Curvature Scale Space

The user of an image database often wishes to retrieve all images similar to the one (s)he already has. Using some features like texture, color and shape, we can associate a feature vector to every image in the database. A fast indexing method then can be used to retrieve similar images based on their associated vectors. We use the maxima of curvature zero crossing contours of Curvature Scale Space (CSS) image as a feature vector to represent the shapes of object boundary contours. The matching algorithm which compares two sets of maxima and assigns a matching value as a measure of similarity is presented in this paper. The method is robust with respect to noise, scale and orientation changes of objects. It is also capable to retrieve objects which are similar to the mirror-image of the input boundary. We introduce the aspect ratio of the CSS image as a new parameter which can be used for indexing in conjunction with other parameters like eccentricity and circularity. The method has been tested and evaluated on a prototype database of 450 images of marine animals with a vast variety of shapes with very good results. Since shape similarity is a subjective issue, in order to evaluate the methilarity retrieval based on shape on a randomly selected small database. We then compared the results of this experiment to the outputs of our system to the same queries and on the same database. The comparison indicated a promising performance of the system.